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Shield AI is a venture-backed defense-tech company developing intelligent autonomy systems, including Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies. The company operates globally with offices across the U.S., Europe, the Middle East, and Asia-Pacific.
As a Staff Engineer on the Platform team, you will be a technical leader responsible for designing and operating the Forge Platform—a distributed systems foundation that underpins how Shield AI develops, tests, deploys, and operates autonomy systems across the organization.
Key responsibilities include:
• Build Kubernetes-native platform services: Develop and operate Kubernetes-based services, controllers, operators, deployment patterns, and runtime integrations supporting distributed workloads across multiple environments.
• Develop distributed orchestration capabilities: Design reusable primitives for authoring, scheduling, and scaling pipeline work.
• Build reliable data-processing infrastructure: Develop platform capabilities for data storage, ingestion, validation, transformation, and governance.
• Develop highly extensible platform components: Provide standardized tooling around authentication, authorization, observation, networking, routing, secret management, and more.
• Create reference architectures: Establish recommended deployment patterns, operating profiles, capacity guidance, benchmarks, reliability practices, and distribution approaches across cloud providers, on-prem, edge, and air-gapped environments.
• Advance observability and operability: Establish end-to-end metrics, logs, traces, structured events, dashboards, alerting, SLOs, operational diagnostics, and runbooks.
• Partner with downstream teams: Work directly with autonomy, ML Ops, simulation, test, infrastructure, product, and customer-facing teams to turn recurring distributed-systems problems into reusable platform capabilities.
You will shape the distributed systems foundation used by engineering teams across the company and delivered into demanding customer environments. Your work will determine how reliably data moves through the organization, how services coordinate across complex environments, how teams execute and recover long-running workflows, and how operators understand the health of mission-critical systems.
Key outcomes include improving platform KPIs around reliability, scalability, and customer adoption; guiding new services to successful platform integration with clear ownership and repeatable deployment patterns; demonstrating and benchmarking the platform across diverse operational environments; and maintaining stable interfaces to instill customer confidence.
REQUIREMENTS:
• Significant experience designing and operating production distributed systems, cloud-native platforms, backend infrastructure, or data-intensive services.
• Strong software engineering skills and a record of delivering production systems in Go and Python.
• Deep understanding of distributed-systems fundamentals, including failure handling, idempotency, consistency tradeoffs, retries, ordering, delivery semantics, backpressure, partitioning, state management, and fault tolerance.
• Experience designing or operating workflow orchestration, distributed job execution, asynchronous processing, event-driven systems, or long-running service workflows.
• Ability to define architecture and technical standards while remaining hands-on in implementation, production troubleshooting, performance analysis, and reliability improvement.
• Experience working across multiple teams to turn recurring infrastructure needs into reusable, well-documented platform capabilities.
• Clear technical communication and the ability to make complex distributed-systems architecture understandable to both specialists and downstream users.
PREFERRED QUALIFICATIONS:
• Kubernetes controllers, operators, Custom Resource Definitions, admission control, scheduling extensions, KubeRay, or workload-management systems.
• Distributed execution and workflow technologies such as Ray, Temporal, Argo Workflows, Flyte, Dagster, Airflow, Prefect, Kubernetes Jobs, or comparable systems.
• Durable messaging and event-streaming technologies such as NATS JetStream, Kafka, Redpanda, Pulsar, RabbitMQ, SQS/SNS, or comparable systems.
• ETL/ELT, batch processing, event-driven data pipelines, CDC, schema evolution, data validation, artifact processing, or large-file transfer workflows.
• Service networking technologies and practices such as Envoy, service meshes, Kubernetes networking, and CNI plugins.
• Observability software such as OpenTelemetry, Prometheus, Grafana, Loki, Tempo, Jaeger, distributed tracing, structured logging, SLOs, service-level indicators, alerting, and incident-management practices.
• Terraform, Helm, ArgoCD, GitOps, Kubernetes package management, repeatable platform distribution, and Infrastructure as Code.
